• The Next Generation of Trust - In India, Investors are Confident and Trusting

    03 May 2018

    While Indian investors are the most likely to say they trust financial services versus other markets surveyed, trust for the financial services sector declined in India since our 2016 survey.

    Indian investors surveyed were much more likely to have a financial adviser than those in other markets. Although trust is still the most important factor in choosing an adviser, retail investors are also strongly motivated by the desire for performance. They are also much more likely than the average investor around the world to recommend their adviser to others.

    The top two reasons Indian investors are likely to leave their financial adviser are underperformance and a lack of communication and responsiveness. Indian investors favor personalized products and technology, and they also place high importance on brand. In terms of building trust, adhering to a code of conduct has a great impact on trust in India. Professional credentials also play a significant role in increasing trust.
  • The Next Generation of Trust - Investor Trust in Financial Services in Singapore

    03 May 2018

    Singapore-based investors expect their advisers to be ethical and well-informed. Almost half of investors in Singapore “completely trust or trust” the financial services sector. Investors in Singapore tend to be significantly younger than those in many other markets. This may partially explain higher trust levels, as younger investors globally are also more trusting of financial services. A majority of investors surveyed in Singapore work with financial advisers, and few investors in Singapore report that they are very confident in their own ability to make investment decisions.

    Some investors in Singapore may question adviser competence. Their primary investment concerns are “My financial adviser making recommendations that result in losses” and “Hiring an unscrupulous financial adviser.” Trust is the most important for investors in Singapore when hiring an adviser. Communication is extremely important to investors in Singapore, and lack of communication is the primary reason they would discontinue a relationship with a financial adviser, although more than half also cite underperformance as a reason for leaving.

    Investors seem to prefer technology solutions over people as a majority say in three years it will be more important to have technology tools to execute their own strategy rather than human advisers. However, when selecting an investment firm, a majority of investors are split between the importance of a “Brand I can trust” and “People I can count on.”

    Read more in the full Market Report PDF below
  • The Next Generation of Trust - People are Trusted More Than Technology in Australia

    03 May 2018

    Retail investors in Australia are some of the most satisfied among those we surveyed. However, even though Australian investors feel the markets are fair, retail investors are much less likely to work with financial advisers than investors in other markets. Although investors are not very confident in their ability to make investment decisions, many still find little need for professional advice.

    By and large, Australian investors also think that advisory fee structures are fair. However, they have less interest in personalized products than investors in any other market included in the survey.

    As in most markets, trust is the most important factor in choosing an investment adviser. However, in Australia people are trusted more than technology. A firm’s brand is also less important than the competency of its employees, and Australians rely on brand less than investors globally.

    Surprisingly, given the overall level of satisfaction for their investment firms, trust is tested in times of crisis, and Australian retail investors are, on average, slightly less confident that their investment firms are prepared for another financial crisis.

    Read more in the full Market Report PDF below.
  • The Next Generation of Trust - Investor Trust in Financial Services in Hong Kong SAR, China

    03 May 2018

    Hong Kong is one of three markets in our survey where trust in the financial services sector is declining. Investors in Hong Kong are highly motivated by returns, and they prioritize performance over trust as a factor in choosing an adviser. Well over half of investors would also terminate an advisory relationship for underperformance.

    Hong Kong investors also appear to be less pleased with their advisers, and fewer than 10% believe that advisers put client interests first. However, few investors in Hong Kong are very confident of their investment decision making, which may indicate why many prefer to invest with the help of financial advisers. Many investors are indifferent to using a robo-adviser and human advisers and in three years believe it will be more important to have technology tools to execute their own strategy rather than a human adviser.

    Investors in Hong Kong also place a high value on professional credentials, ongoing professional development, and firms that adhere to a voluntary code of conduct. When selecting an investment firm, the majority of investors prefer a “Brand I can trust” over “People I can count on.”

    Read more in the full Market Report PDF below


  • Capital Formation - Call for Information

    28 Apr 2018

    In this call for information, we are asking you to share your knowledge of your local market and region, and the issues surrounding public vs. private capital formation.
  • Presentation Deck of the SIDC - CFA Society Malaysia ARX Seminar - 'Future State of The Investment Profession"

    27 Apr 2018

    The Future State of the Investment Profession (FSIP) study released by CFA Institute describes an industry at an existential crossroads. It warns that investment industry leaders who fail to transform their business models may jeopardize the future of their firms. To "future proof" themselves, FSIP provides a series of planning scenarios that combine magatrends that impact all industries with forces specific to the investment industry. These scenarios can be used as tools for leaders to steer the future of their businesses and ultimately improve outcomes for investors.
    Among the megatrends identified are technological advances, redefined client preferences, new macroeconomic conditions, different regulatory regimes reflecting geopolitical changes, and demographic shifts.
    More insights can be found in the attached presentation deck.

    Joseph Wong    Rich Blake, Brindha Gunasingham, PhD, CFA
    11 Apr 2018

    Based on the paper “Business Cycles and the Cross-Section of Currency Returns” by Steven J. Riddiough and Lucio Sarno, available at

    This paper was recently recognized as the CFA Institute Asia-Pacific Research Exchange Best Paper at the 7th Annual Financial Research Network (FIRN) Conference. FIRN is a network of finance researchers and PhD students across Australia and New Zealand.

    Originally published in the spring of 2017 and recently updated, “Business Cycles and the Cross-Section of Currency Returns,” by co-authors Steven J. Riddiough (University of Melbourne) and Lucio Sarno (Cass Business School and CEPR), makes a case for a genuine connection between currency returns and the waxing and waning of countries’ business cycles worldwide. According to Riddiough and Sarno, excess returns can be gleaned—and quantified in a risk-compensation context—by buying and selling a crosssection of currencies relative to the strengths or weaknesses of their country’s economic cycles, a finding that flouts decades of research suggesting the absence of a link between
    macroeconomic variables and currency fluctuations.

    Among the drivers of such a strategy is the use of the spot exchange rate, demonstrably more 
    predictable than interest rate moves. Also crucial to understanding the source of returns is the observation that the most robust currency appreciations occur when cross-border business cycles are diverging. Although buying the currencies of strong economies and selling the currencies of weaker ones might seem intuitive, there is no shortage of real or theoretical headwinds facing anyone who might attempt it. The spot market is notoriously and exceptionally volatile, and the nature of forecasting business cycles represents its own deeply explored yet only partially understood pursuit. Empirically, Riddiough and Sarno have tilled new ground.

    In their paper, Riddiough and Sarno refer to academic research that supports the notion of a strange disconnect between macro fundamentals and currency exchange rate moves, particularly in short (one month) and intermediate (one year) time horizons. Why wouldn’t a country’s economic growth rate underpin—or indeed, help predict—the fluctuation of its currency, just as a company’s fundamentals would have an influence on share price? With this counterintuitive reasoning as a talisman, Riddiough and Sarno embark on a journey to resolve what others before them have found so puzzling. Employing that broadest measure of macro conditions—the business cycle—they examine how this basic concept gets measured in the first place, whether it even can be measured, and, if so, whether there is some way to harness it for alpha production.

    Step one for Riddiough and Sarno was to determine how best to take the extensive data from a cross-section of 27 countries over three decades and come up with a measure of when each country’s business cycles started, when they halted, and how long they lasted. Even arriving at a commonly accepted measure for business cycles—the so-called “output gap,” which is a country’s percentage deviation from its long-term trend—proved challenging. Leaving aside the not-uncommon idea that cycles are too mercurial to pin down, there was the vexing conundrum of sifting through and amalgamating various output-gap measurement techniques (quadratic data-spanning filters versus linear counterparts). The authors needed to produce a drop cloth of macroeconomic conditions
    upon which to portray a currency trading strategy conducted in a long-term portfolio setting. By running numbers through the prism of a series of five simulated portfolios (set up in contrast, with degrees of weak and strong currencies), the authors were able to take into consideration such concepts as relative performance, risk compensation, and diversification benefits that could be associated with currency returns. In other words, this was no carry trade. The question then became, “If business cycles could predict currency returns in a portfolio setting, could an investor capitalize?”

    Spot exchange rate predictability was evident in both a cross-section and a time series analysis of the countries’ business cycles. In summary, buying and selling based on business cycles not only generated high returns but the outperformance was not correlated with the most common currency strategies, such as long Australian dollar/short Japanese yen. According to the authors, “Currencies issued by strong economies (high output gaps) command higher expected returns, which compensates more risk-averse investors in weak economies.” The authors go on to say, “Our research suggests a strong predictive link from business cycles to currency returns, and raises questions as to why our results differ from those in the long-standing international macroeconomics literature.”

    One reason may lie in the use of spot rate moves to extract excess return, and not via commonly used derivatives. In the aforementioned carry example, the trade would remain static; those long and short positions wouldn’t change over time even though business cycles or output gap differentials would.

    “An output-gap investor would have taken long and short positions in both the Australian dollar and Japanese yen as their relative business cycles fluctuated,” the authors claim. Returns, they emphasize, mainly come from the divergence in business cycles. Using data and a rigorous process, investors can define cycles and exploit their turns.

    Where once investors had only a few “risk factors” to choose from—growth, momentum, size, and value—now they have dozens and must add business cycles to the growing list. Because an output-gap strategy has such low correlation with other currency strategies, investors who once only considered currency exposure as something to be hedged might be open to using it as a source of alpha generation, particularly at a time when large segments of the stock and bond markets are reaching boiling points and perhaps pointing toward the start of a new set of intraglobal cycles to come.

    “At the heart of almost any model of currency returns is a tight link between the macroeconomy and exchange rate returns,” Riddiough explained recently in an email. “But it’s taken a long time to pin down this relationship empirically. In this paper, we’ve demonstrated that the link is real, spot returns are predictable, and the resulting investment strategy is unlike any we commonly employ in currency markets.”

    Riddiough and Sarno have found a relationship between macro fundamentals and exchange rates—a unique, underexploited source of returns. The analysis has been completed using data from markets around the world, including Australia, Japan, and New Zealand, demonstrating that this is not a phenomenon confined to the United States or even the Eurozone. Global and Asia Pacific investors, hungry for diversification, should take note.


    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.
  • Understanding the Investment Fundamentals of the Telecommunications Sector. A part of the series "Sector Analysis: A Framework for Investors"

    Joseph Wong    Alan Lok, CFA, Eunice Chu, Guruprasad Jambunathan
    10 Apr 2018

    For investors exploring the telecommunications sector, it is important to be aware of the key economic, operational and regulatory factors influencing these firms. These not only vary from country to country but also from company to company, depending on the kind of service that is being provided – fixed line, mobile or a combination of the two. Common to all are the opportunities afforded by the growth in data and the proliferation of online services. For operators in developing markets, lower penetration rates offer long-term opportunities. Meanwhile for operators in the  developed world, staying relevant by keeping pace with technological advancements is vital. In general, the sector is marked by intense competition, hefty capital expenditure requirements (at least historically) and rigorous regulatory intrusion.

    There are three listed telecommunication stocks in the FTSE ST All-Share index, with a net market capitalisation of S$28.6 billion, and they accounted for 7.5% of the index as at 31 Jan 2018*. Of the three, SingTel is the largest constituent  company, representing about 90% of the Singapore telecommunication sector by market capitalisation.

    The sector analysis for REITs can be found on ARX here: 

    To read more, download the full sector analysis for the telecommunications sector with accompanying question bank below. 

    This publication qualifies for 0.5 CE credits under the guidelines of the CFA Institute Continuing Education Program.
  • Understanding Real Estate Investment Trusts (REITS) Sector Analysis: A Framework for Investors

    Joseph Wong    Alan Lok, CFA, Eunice Chu, Guruprasad Jambunathan
    10 Apr 2018


    The key to a company’s success depends on how well it executes its business model. This calls for optimising the allocation of limited resources to generate sustainable cash flows, for investing in new products, technologies, and services in responding to the wider competitive landscape or societal changes and mega trends, as well as for devising appropriate responses in the face of an evolving macroeconomic, regulatory, and political environment.  

    Different industries often require very different business models; and even within the same industry, the model that does add value to the business may vary somewhat from company to company.  

    To help investors undertake proper due diligence on a company, we have generated a framework of analysis designed to tease out the following: (1) whether the pertinent factors favour the firm in question; and (2) whether management is effective in executing its business model or value-generating strategies, while responding appropriately to its external environment.

    This framework is customised to specific sectors and incorporates interviews with professionals within those sectors. 

    REITs are vehicles that own and typically operate a portfolio of income-yielding real estate assets. Modelled along the lines of unit trusts, REITs allow for funds to be pooled from a group of investors. Such a structure provides retail investors with several advantages: a low-hurdle of entry and exposure to a diversified pool of real estate assets with a high level of liquidity, which would not otherwise be possible with direct investing. 

    Most REITs are publicly listed, and declare above 90% of their earnings as dividends to fulfil certain benefits accorded to REITs by the local securities regulator. As such, REITs provide a stable source of recurrent income, which serves as a yield play rather than an investment avenue for reaping capital gains. We believe an effective and accurate fundamental analysis can help the retail investor determine if the recurrent income is stable and/or trending upwards over the long term. 

    A REIT generally focuses on a specific category of property for investments.  Some common classifications of REITs include: Office & Commercial REITs, Retail REITs, and Industrial REITs.

    To read more, download the full sector analysis for REITs with accompanying question bank below.

    This publication qualifies for 1.0 CE credits under the guidelines of the CFA Institute Continuing Education Program.

    Joseph Wong    Rich Blake, Asjeet S. Lamba, PhD, CFA
    27 Mar 2018

    Based on the paper “The Value of Institutional Brokerage Relationships: Evidence from the Collapse of Lehman Brothers” by Jianfeng Shen, Jerry T. Parwada, Kok Keng Siaw, and Eric K.M. Tan, available at

    This paper was recently recognized for excellence by the CFA Institute Asia-Pacific Research Exchange (ARX) at the 7th Annual Financial Research Network (FIRN) Conference. FIRN is a network of finance researchers and PhD students across Australia and New Zealand.

    Institutional brokerage has always been a many-splendored thing. Analyst recommendations, IPO allocations, block order execution, networks for sourcing liquidity—these and other equity-trading–tied services were the lifeblood of Wall Street. Fund managers, via directed trades, lapped it all up and in return fed bank-owned brokers billions in commissions. Then came the Dodd–Frank Act, which led banks to scale back on capital-at-risk, and an even more dramatically disruptive trend: the rise of automated, or algorithmic, trading.

    Today, the interaction between money managers (“buy side”) and institutional brokers (“sell side”) is focused primarily on managers getting access to bank-run electronic trading venues known as “dark pools.” Though the institutional equity trading business has shrunk to a shell of its former self, an unshakable symbiosis remains between the two “sides.”

    One can never underestimate the intangible benefit of a longstanding, trusted relationship. And certain mutual fund managers may want to consider those benefits when deciding whether to dole out commissions or reel them in, according to Shen, Parwada, Siaw, and Tan, whose paper, “The Value of Institutional Brokerage Relationships: Evidence from the Collapse of Lehman Brothers,” is the subject of this ARX Practitioner’s Brief.

    “There is still much that we do not know about how fund managers’ performance is related to institutional brokers because it is difficult to measure relationship capital,” the authors write as they tee up their research work, which cleverly holds a mirror up to one question—“What do brokers really offer fund managers?”—by asking another instead: “How would a fund manager suffer if one of their trusted brokers suddenly was removed from the equation?

    Although fund managers have become less reliant on sell-side research, billions in commissions
    still flow from fund managers to brokers. Analysts can’t offer the kind of insider intelligence that they once could (because of the US SEC’s Regulation Fair Disclosure), but information is still to be had, say, from a bank-facilitated meeting with a management team.

    The reality is there are thousands of stocks out there to be covered but only so many analysts a buy-side firm can afford to employ. Unavoidably, fund managers continue to steer trades to institutional brokers in exchange for a bundle of premium services, which may include research, execution, meetings, conferences, and a certain level of recognition on the part of fund managers that they can rely on their trading partners in a pinch. But does all of that translate into better investment performance? If not, what’s the point of having an institutional broker?

    The collapse of Lehman Brothers on 15 September 2008 was the largest bankruptcy in US history. For the authors, it was the perfect setting to answer the question, “What happens to fund managers when one of their key brokers goes out of commission?” Using data from US SEC Form N-SAR (through which mutual funds disclose to whom trading commissions are paid), the authors identified more than 730 mutual fund clients of Lehman Brothers just before the crisis; they then compared that group’s performance over a 48-month period (September 2006 through August 2010) to 366 non-Lehman mutual fund clients.

    It is worth noting that Lehman’s brokerage arm did not instantaneously vanish in the collapse. The authors assert a causal impact on the Lehman mutual fund clients not from Lehman’s disappearance but rather from the severe disruption of its brokerage unit. Disrupted is one way to put it: the unit was liquidated and absorbed abruptly and chaotically into Barclays Capital. Trust evaporated. Of 25,000 employees, one-third were let go more or less immediately and another one-third left within two years. Still, one would think that certain key people were retained and to some degree some form of value was rendered. Besides, most fund managers had plenty of other brokers in their stables, and further, what value did brokers even provide in the first place?

    For fund managers, assessing the value added by their institutional brokers had long been a challenging exercise. Perceptions at the time of the Lehman collapse were largely that the value of such brokers had already diminished. And yet …

    The authors found that certain types of fund managers experienced a decrease in performance when Lehman became severely impaired. They pointed to monthly return lags averaging as much as 70 basis points per month relative to those fund managers who weren’t affected. Hardest hit were smaller firms that by design had exceedingly concentrated brokerage networks; also hurt were those firms that specialized in small-cap investments and thus were overly reliant on the deeper breadth of sell-side research. Put another way, these types of small/small-cap–focused firms were the ones extracting the most value from their broker relationships. Portfolio managers, especially smaller ones, strategically channeled a large portion of orders to a few brokers to get more bang for their commission bucks. And this reliance came at a risk. Damage to one key broker resulted in a reduction in alpha.

    Human capital shouldn’t be underestimated. Trusted brokers leverage myriad relationships built up over time to incalculable effect—sometimes you really don’t know what you’ve got until it’s gone. Downsizing doesn’t always pay dividends. It’s still important, particularly for small-in-size/small-cap–focused fund managers, to maintain close ties with institutional brokers. Although certain funds may resort to establishing new relationships, doing so involves significant switching costs and the forfeit of any relationship capital developed in the prior relationships. Overall, relationships still matter—perhaps to an ever-lessening degree in equities, but they still matter. Lehman’s collapse made for a fine experiment. But now, 10 years later, the authors’ findings, while surprising, nevertheless ring increasingly irrelevant with each passing day as more buying and selling occurs autonomously via algorithmic trading.

    The authors challenge their peers to take up similar research in fixed income, where trusted human capital remains truly valuable. The give-and-take between sell side and buy side in fixed income would seem exceptionally rife for further exploration. But that is another story.


    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.

    27 Mar 2018

    Based on the paper “Heterogeneity in How Algorithmic Traders Impact Institutional Trading Costs” by Tālis J. Putniņš and Joseph Barbara, available at

    This paper was recently recognized for excellence by the CFA Institute Asia-Pacific Research Exchange (ARX) at the 7th Annual Financial Research Network (FIRN) Conference. FIRN is a network of finance researchers and PhD students across Australia and New Zealand.

    Traversing the dense, tangled underbrush of an otherwise mostly explored section of securities terrain—the impact of automated, computerized trading—two researchers have demonstrated why it doesn’t pay to ignore the nuances of a complicated subject. Literally, it can cost billions to not heed the observations of authors Putniņš and Barbara, whose paper, “Heterogeneity in How Algorithmic Traders Impact Institutional Trading Costs,” is the subject of this ARX Practitioner’s Brief.

    The July 2017 paper is a wake-up call for institutional investors who may not be as vigilant as they think they are when it comes to getting best execution on block orders, if only because their defenses might well be focused on the wrong bad actors, that is, high-frequency traders (HFTs). HFTs, argue Putniņš (University of Technology Sydney) and Barbara (Australian Securities and Investments Commission), are unfairly stigmatized and singled out among computer-program–based or algorithmic traders (ATs) for driving up big-block trade implementation costs when in reality, according to an exhaustive study of trading data, their impact is negligible.

    In support of their argument, Putniņš and Barbara fully mapped and surveyed an algorithmic trading community comprising both HFTs, who transact a large number of orders at eye-blink speeds, and non-HFTs. In the process, they uncovered a variety of species and motives, some of which are even beneficial to institutions. On the surface, the ground the authors covered would seem cut and dried: grievances about HFTs have been voiced repeatedly, to the point where no one questions who in this narrative wears the black hat and who wears the white.

    What the authors sought to understand was whether the complaints against HFTs had merit. Was there more to the story than what generally has seeped into the mainstream media via books such as Michael Lewis’ Flash Boys?

    The rise of electronic equity trading venues at the dawn of the 21st century emptied the trading floors, drove down execution costs, and opened the way for technological advancements, such as order-implementation speeds measured in milliseconds, that few could have ever imagined. By the time of the 2010 flash crash, the fundamental manner by which stocks were traded had radically changed. Although a few die-hard specialists were still clinging to their Big Board posts back on that spring day in 2010, the flash crash made it abundantly clear that algorithms had taken over. At the center of regulatory scrutiny post-flash crash was high-frequency trading, the best-known and most controversial form of algorithmic trading.

    With alpha scarce and trading venues fragmented, fund managers increasingly focused their energy on improving execution costs. For decades, the buy side railed against specialists front-running their institutional orders. Now, institutions face a new predator on their blocks: HFTs. These automated strategies account for more than half of the total volume during any given session, and some institutional investors claim they impede liquidity.

    As a result of concerns about being preyed upon, institutional investors are forced to break large orders into smaller pieces that need to be traded across multiple venues, making them more susceptible to HFTs. In turn, new liquidity pools and networks have been created to provide a safe space. Yet, as Putniņš and Barbara point out, some studies show that, at best, high-frequency trading and algorithmic trading lower spreads and improve price discovery, and at worst, represented a benign force. So are HFTs good, bad, benign, or what?

    Putniņš and Barbara created a data cross-section reenacting trading of the largest 200 Australian equities (ASX 200 Index constituents) over a 13-month period (1 September 2014 through 30 September 2015), amounting to 273 trading days.

    Using unique trader-identified regulatory audit-trail data, they identified a subset of 187 of the most active nondirectional traders (AT/HFT) and measured their activity (roughly 25% of Australian volume on any given day) in terms of the impact on the execution costs for institutions, which control about 80% of Australian large-cap stocks. “Origin of order” identifiers, collected by the Australian Securities and Investments Commission, allowed the authors to reassemble smaller (child) orders back into larger (parent) ones.

    Upon close inspection, the AT/HFT gang of 187 proved decidedly heterogeneous. Putniņš and Barbara categorized these traders across a spectrum, ranging from those who drove costs up the highest (toxic) to those who lowered them the most (beneficial).

    The 12 most toxic traders increased the average order-implementation shortfall cost by 10 basis points or nearly double the cost without the harmful behavior. At the same time, the 14 most beneficial traders systematically decreased costs, effectively, in aggregate, countering the negative impact. However, this offset in aggregate would not have come as any consolation to those individual buyers and sellers specifically impacted by the toxic traders. “An investor that disproportionately interacts with harmful AT/HFT faced higher costs,” concluded the authors.

    Interestingly, HFTs were no more likely to be toxic than non-HFTs. And even those ATs/HFTs who drove up costs may have done so unintentionally, merely by trading on the most common entry and exit signals, behavior that could be described not so much as exploitative as lemming-like.

    First, for buy-side asset managers, it bears underscoring that execution matters. Potentially large cost savings can be realized from trading in a manner that avoids overexposure to toxic counterparties. Such savings could mean the difference between a fund that performs well and one that underperforms.

    Second, in terms of execution strategy, more caution should be exercised in smaller stocks, where toxic traders tend to be more active.

    Third, effort spent avoiding HFTs may be in vain because many HFTs are beneficial and can reduce institutional execution costs. At the same time, toxic non-HFTs should be avoided if one wants to minimize execution costs.

    Finally, from a regulatory perspective, the empirical measurement tools featured in this research could be used to better monitor markets and identify predatory trading behavior.


    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.
  • Sustainability Bonds: Risks and Opportunities for Investors

    10 Dec 2017

    Whether we’re talking about instruments that are formally designated as "green bonds" or unlabeled climate-aligned issuances that still fall under the environmental, social, and governance (ESG) umbrella, the demand for such sustainability-tied debt is rising. The green market can no longer be dismissed as a feel-good fad propped up by regulatory agendas. Although just a tiny sliver (less than 1%) of the roughly US$100 trillion global bond market, sustainability bonds, which include labeled and unlabeled green bonds as well as social bonds, offer a viable way forward for a wide-reaching cadre of constituents (e.g., banks, investors, governments, and corporations) looking to fund environmentally favorable projects, and to do so at a profit.

    The size of the market for global labeled green bond issuances increased to US$81.6 billion in 2016, nearly double the amount issued the year before. China led this increase, accounting for more than a quarter of the 2016 total. The primary market may not be a place to hunt for juicy spreads, but pricing signals in the burgeoning secondary market tell a different story, suggesting that alpha can be gleaned from all that green.

    By Rich Blake. Rich, a CFA Institute contributor, is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News, and Institutional Investor.
  • Family Controlled Firms in APAC: 10 Focus Areas for Investor Due Diligence

    07 Nov 2017

    CFA Institute has recently released a 112-page report Corporate Governance far Asian Publicly Listed Family-Controlled Firms which identifies the strengths and challenges of the publicly listed family firm model in Asia. The full report is available at:

    The report is aimed at institutional and retail investors and outlines areas to focus due diligence when investing in family-controlled companies in Asia.

    In the attached guide we take highlights from the report to identify the top 10 issues that investor may encounter when investing in family firms and offer steps to mitigating those risks.
  • Practitioner's Brief: Friction Key to Exploiting Stock Market Inefficiencies

    Tom Berry    Söhnke M. Bartram, Mark Grinblatt
    29 Oct 2017

    The paper is one of a handful that seeks to address why some markets are less efficient than others. Although it is conventional wisdom, this premise is not universally accepted.

    Some academic research papers support the concept that developed markets are no more efficient than emerging markets. Authors Bartram and Grinblatt dispute this. Others have argued that investors rely too heavily on Fama’s Efficient Market Hypothesis (EMH)—that is, that stocks always trade at their fair value—rendering any effort to profit from mispriced stocks an exercise in futility.

    On the one hand, the authors ask, if no one can ever profit from active management, then what magical force exists to drive prices to fair value? They argue that a passive investor will buy the index fund, whatever the prices of its component parts. Active managers, who have performed relatively poorly of late, may well have another cycle to show off their talents—and so will want to take note of where and when mispricings and market inefficiencies cross paths.

    Bartram, of the University of Warwick, and Grinblatt, of UCLA, began their work by going through a database of company financials that went back more than two decades to capture all the information about the companies that would have been known at the time.

    They constructed a robust set of synthetic portfolios—nearly 26,000 stocks from three dozen countries—to theoretically trade on mispriced companies, with a few unique layers of variables to provide reality checks. Trading signals, suggesting a clearly identifiable deviation of a stock’s price relative to its estimated fair value, were built using international point-in-time accounting data covering 21 company-specific metrics. Trading activity was replicated with transaction cost data from Elkins McSherry, the gold standard for tracking such expenses.

    Transaction cost data included explicit commissions and fees as well as harder-to-quantify market impact costs. These costs were converted to alpha reductions using portfolio-turnover approximations, that is, two-way turnover.

    The results of running the mispricing replicating portfolios (and incorporating simulated buy/ sell executions) were definitive: Emerging and certain developed Asian markets were shown to be relatively less efficient in countries with quantifiable market frictions—particularly trading costs—that deter arbitrageurs. “If profits to trading strategies based on mispricing estimates are a measure of market inefficiency, then profits should vary across countries as a function of transaction costs, short sales restrictions, and other country characteristics that might influence limits to arbitrage, thereby impeding the process that makes a country’s stock prices reflect fair value.”

    In regions where markets are most efficient, Bartram and Grinblatt caution that investors need to be aware of the costs of active management, noting that it is unlikely the fees associated with active management will outweigh its value. A caveat here, though, is that the data studied were annual accounting data. Conceivably, some improvement in the alpha may be generated by the strategy, even in the most efficient global markets, when using quarterly data. And there’s a catch as well: The least efficient markets, where the alpha opportunities may be the largest, can easily be eroded by those frictions—in other words, super-sophisticated investors are steering clear for good reason.
  • Corporate Governance for Asian Publicly Listed Family-Controlled Firms - Full Report

    Tom Berry    Tony Tan, DBA, CFA, Fianna Jurdant
    26 Sep 2017

    There is a gap in the literature concerning regulatory responses and approaches to corporate governance of controlled family firms. This report reviews the literature to develop an overview of the economic landscape of publicly listed family firms in Asia and to demonstrate the importance of these entities to the region and beyond. Another objective is to illustrate the challenges that confront policymakers in developing corporate governance policies relevant to publicly listed family firms. Discussion in the report indicates two significant hurdles for policymakers. First, there is a lack of consensus on a universal definition of a publicly listed family firm. Second, publicly listed family firms should not be perceived as a homogeneous group over which a “one size fits all” corporate governance blanket can be cast.

    The focus of the report then shifts to highlighting how an effective corporate governance system can improve performance and create value by reducing the cost of equity and reducing capital waste. The discussion examines how properties of publicly listed family firms can enhance shareholder wealth or lead to the expropriation of wealth from minority owners. The final objective of the study is to report key observations from a series of case studies spanning 14 Asian economies that cover a range of corporate governance issues central to publicly listed family firms. The case studies provide insight into current practices of Asian publicly listed family firms that may require the attention of policymakers. The issues raised in this report provide the initial building blocks for developing a more extensive road map to underpin a comprehensive analysis. Findings from this analysis can provide the foundation for evidence-based policy recommendations specific to family firms, which are a significant aspect of the Asian economic landscape and a key to the region’s current and future prosperity.
  • Corporate Governance for Asian Publicly Listed Family-Controlled Firms - Executive Summary

    Tom Berry    Tony Tan, DBA, CFA, Fianna Jurdant
    18 Jul 2017

    Good corporate governance is increasingly considered one of the prime drivers of business success. Through transparency, equitable treatment of all shareholders, and a robust system of sound practices and procedures, good corporate governance can enhance performance and growth, both in the individual firm and at the national level.

    A solid corporate governance framework is particularly important for family firms, which face unique challenges as they balance the advantages and disadvantages of family involvement in the business.

    Based on an analysis of 56 family-controlled listed companies in 14 jurisdictions,* the CFA Institute report, Corporate Governance for Asian Publicly Listed Family-Controlled Firms, identifies opportunities to enhance corporate governance structures for family firms in the region. The report reveals how effective corporate governance can help these companies—and the regions in which they operate—continue to achieve economic success.

    Over the last few decades, Asian family firms have played a pivotal role in fueling the region’s economic growth, and their influence will continue to rise. By 2025, the number of firms in Asia with revenue exceeding USD1 billion is expected to be nearly equivalent to that in developed economies globally. Family firms will represent 75% to 80% of those entities.
    However, the growth of Asian economies in recent decades has been largely propelled by low labor and production costs. As the performance of Asian economies begins to mirror that of developed economies, their future capacity for growth will not be sustainable if they are competing on cost alone. To remain competitive, Asian family firms must innovate, expand outside of traditional markets, and professionalize, which will necessitate the tapping of global talent and capital. This will put pressure on these firms to have a corporate governance structure in place that can meet international standards and investor expectations.

    Challenges of Internationalization
    Between 2000 and 2010, the total market capitalization of Asian family firms grew significantly. A major driving force behind this was an entrepreneurial desire among Asian family firms to use capital market funding to expand in new markets, with the number of listed family firms increasing 62%. As more family firms use capital markets to fund their internationalization plans, they will face the challenge of developing sound corporate governance frameworks that meet the needs of the heightened regulatory environment and the scrutiny that comes with being listed.

    Challenges of Professionalization
    Although Asian family firms prefer to pursue family-management succession plans, many recognize the need to capitalize on external talent to meet future business pressures. Efforts to professionalize a family firm, however, may be double-edged.

    On the one hand, professionalization might boost a firm’s effectiveness. On the other hand, professionalization might give rise to additional agency costs, such as the need to offer incentives to align the interests of professional management with those of family members. If a family firm is to realize the benefits of bringing in external talent, then that incoming management will need the freedom to do the job for which they were hired. Defining an optimal equilibrium between family culture and external professionalism is therefore imperative to facilitate future value creation without incurring greater expenses.

    Challenges of Dispersed Ownership
    The average percentage of family ownership of large-size family firms in Asia is substantially lower than that seen in their European and North American counterparts. This implies increased ownership diversity, which can result in two major issues. First, with a widely dispersed minority ownership structure, the entity is potentially exposed to greater majority/minority owner conflicts. Second, Asian family owners who wish to expand their businesses while still retaining control may rely more on creditors than on further equity dilution. This could potentially lead to greater shareholder/creditor conflicts. Family firms should develop corporate governance policies to address these concerns.

    Research is inconclusive on whether the family-firm construct enhances or diminishes corporate governance practices. In theory, the long-term horizon and closer alignment of principal-agency interest in family firms should improve corporate governance. However, those same features could prove problematic by increasing risk, whether as a result of a lack of transparency, entrenchment, or wealth expropriation from minority owners.

    A solid corporate governance framework is essential for family firms to effectively balance the advantages and disadvantages of family involvement in the business. Combining governance, management, and ownership in the hands of family can bring benefits, but this centralized decision-making structure inevitably brings risks. Sound corporate governance practices can help family firms include different perspectives on their boards, which can mitigate risks. Moreover, such practices can help family firms balance the interest of different stakeholders, a task essential to the long-term sustainability of these entities. As well, sound corporate governance practices can help family firms reduce their cost of capital and reduce capital waste, making them more attractive investment targets and more competitive entities.

    The complex challenges facing publicly listed family firms in Asia are influencing the underlying corporate governance frameworks of those firms. Through a holistic understanding of corporate governance features supporting firm performance and value across the region, these firms will be better able to address the difficulties they face and to thrive in the future. The development of policy recommendations that assist in enhancing the corporate governance practices of Asian publicly listed family firms will also increase protection for minority owners from wealth expropriation by the majority, controlling family owners.

    Learn more about how corporate governance can impact family firm value and success at
  • Interest in ESG Investing Poised to Grow Further in Asia Pacific

    10 Jul 2017

    • CFA Institute further extends ARX ESG Investing Series to Singapore to discuss motivations for ESG integration in the region.
    • Panelists from S&P Dow Jones Indices, City Developments Ltd., ADL Infra Capital Myanmar, and ESGuru spoke to a full house of CFA Institute members and local practitioners on developments in green finance.
    • Participants concluded that despite challenges, green finance would continue to attract investor interest; supply of green instruments needs to catch-up.
    • Social and governance considerations still in their infancy in the region.
    • The question of alpha potential inconclusive.
    Dr. Tony Tan, CFA, head, global society advocacy engagement at CFA Institute kicked off the May 11, 2017 lunch-time talk entitled ‘Is green finance a fad? Or does it possess alpha potential?’ The event, organized by CFA Institute and CFA Society Singapore follows the first of the ARX ESG Investing Series, hosted in Hong Kong. This series has been developed in response to demand for ESG-related research on research platform, Asia-Pacific Research Exchange (
  • CFA Institute Officially Launches the Asia-Pacific Research Exchange

    25 Jun 2017

    Unveiled at a global launch event held in Hong Kong on June 20, 2017, the Asia-Pacific Research Exchange or ARX (, is a user-driven community hub that gathers scholarly papers, research reports, articles and blogs, conference presentations and datasets from both practitioners and academics.

    Commenting on ARX, Nick Pollard, Managing Director, APAC, CFA Institute, explained that: "We are responding to a need. With ARX, we provide a platform that allows people to share their knowledge and wisdom." He continued: "ARX is a community that supports the development of healthy capital markets. It is specifically dedicated to the Asia-Pacific investment management industry, promoting excellence and educating market participants.”

    During its soft-launch phase, ARX quickly caught the attention of CFA Institute members and charterholders, industry practitioners, and academics. Governments have also been using the platform, as have regulators. Indeed, the period leading up to the official launch has seen ARX accumulate 36 institutional contributors and over 2,500 research reports and articles. This early adoption supports the belief that ARX will become a catalyst for robust conversations about what is important in the Asian investment management industry.

    From a practical nature, ARX is also easy to use, with Scott Lee, Director, Asia-Pacific Research Exchange, guiding attendees through the site's key features. He also underlined the fact that ARX is a free service and registered users will gain unrestricted access to all content on the platform and be the first to hear about CFA Institute events.

    Scott also highlighted the success of the online-to-offline (O2O) capabilities of ARX, with contributors able to organize events around a particular piece of research. To this end, he introduced Hong Hao, CFA, Managing Director and Chief Strategist, Bank of Communications International, who took his widely read paper, Post-Brexit: How to Trade China, and presented it to CFA members at the first-ever ARX O2O event, held in Shenzhen in late 2016.

    Further evidence of the collaborative potential of ARX came from Esmond Lee, Senior Advisor, Hong Kong Financial Services Development Council (FSDC), who pointed out the strategic partnership that exists between his organisation and CFA Institute: "In the past few months, we have shared research and subsequently co-hosted a number of events that have explored topics such as compliance, green finance and ESG for state-owned enterprises."

    Turning to CFA Institute members and how it meets their particular demands, Yin Toa Lee, CFA, ARX Society Engagement Council and Representative of the Hong Kong Society of Financial Analysts (HKSFA), explained how he has used the service to successfully share his doctorate thesis with a wider audience than would otherwise have been the case: "In a short period, I have received several hundred views from a cross section of industry participants – both here in Asia and farther afield."

    To conclude, Mary Leung, CFA, Head, Standards & Advocacy APAC, CFA Institute, explored what happens next: "We are committed to improving the platform's features, and future developments will include public profiling, private messaging and discussion forums. Also, we are pursuing new strategic partnerships and plan to deepen levels of user engagement."
  • Leviathan Inc. and Corporate Environmental Engagement (Video Presentation)

    20 Jun 2017

    State-owned enterprises (SOEs) have been criticized for poor governance and questionable efficiency. In a recent paper titled ‘Leviathan Inc. and Corporate Environmental Engagement,’ Dr. Pedro Matos from the Darden School of Business, University of Virginia, and his colleagues from the University of Hong Kong and Singapore Management University conducted an international study of the impact of state ownership on a firm’s engagement in environmental, social, and governance (ESG) issues. 

    There has been significant debate on the effects of ESG issues on shareholder value. In this paper, it was found that SOEs are, in fact, more engaged in environmental issues and, more importantly, this engagement does not come at the expense of shareholder value. Furthermore, SOEs are also more engaged in social issues, but they do not reveal better corporate governance performance.

    This is a recording of the presentation hosted by CFA Institute, HKSFA, ACCA, FSDC, HKIRA, and HKU SPACE Executive Academy on June 6, 2017 at HKU SPACE Po Leung Kuk Stanley Ho Community College in Hong Kong.
  • PRACTITIONER’S BRIEF:  Turn Seemingly Irrelevant Beta Into A Potentially Powerful Predictive Tool Using The Implied Cost Of Capital

    15 Jun 2017

    Still widely considered as bedrock financial theory yet often criticized, CAPM is a simple, formal methodology to price securities. Its fundamental idea of systematic risk is reflected in all modern asset-pricing theory. Because math-based models are built on sets of assumptions (e.g., “markets are frictionless and efficient”) that may not always reflect reality, all models are, to some degree, suspect. Such models are still relevant, however, in trying to gauge the differences in risk premiums of individual stocks. CAPM dictates that beta is the solely relevant measurement of a given stock’s risk—its co-movement with the market—relative to the movements of the market as a whole.
    The foundation for asset-pricing theories such as CAPM is the Efficient Market (EM) hypothesis, which, over the years, has taken on water like the hull of a leaky boat. Skeptics, have poked holes in the EM hypothesis and cast CAPM in a contradictory light. Numerous studies have shown that beta, which came from CAPM, loses relevance when closely scrutinized. Shi and Xu are careful to set the stage by conceding from the outset that beta in and of itself exhibits weak power in explaining return differences of individual stocks. However, they also seek the source of this weakness: Could there be a glitch with standard “cross-sectional regression tests” that shows, when comparing one stock to another stock, statistical beta estimates to be overly noisy and/or limited measures of expected return? Might these backtests have a fundamental design flaw? And if so, is there a better proxy for expected return?
    With a long-horizon investing approach in mind, Shi and Xu assert that most beta-bashing research shares a common denominator: the use of short-term testing methods to demonstrate the inconsistency of beta patterns. A common example, they find, is inputting the next month’s realized return as a proxy for the (retroactively assigned) expected return. Shi and Xu believe that CAPM may be better suited to capturing the risk-return relation in the long-run. To refashion CAPM to achieve this goal, they create a new way to model longer-horizon expected returns in their tests by having the future Implied Cost of Capital (ICC) play the part of expected return. “ICC is in the same spirit as internal rate of return,” they write. “It is perceived average return over time even when actual future expected returns might be time varying.” To test their theory, the authors examine all U.S. public companies on major exchanges (stripping out financial companies due to their overabundance of leverage, which skews the model) and pulling together, as a proxy for future cash flows, a mountain of analyst earnings forecast data (annualized). They supplement those data with long-term growth-rate assumptions for certain industries, forming a 710,840-variable dataset. The key output is the estimated ICC, which is calculated by asking what expected returns have investors used to discount future cash flows (approximated in part by the analysts’ earnings forecasts) to observe the current equity prices. Although the use of actual future returns might seem like a reasonable means of representing expected returns, an approach in which ICC is used as a proxy for expected return is, the authors insist, a better estimate when running models that aim to drill down into the explanatory power of beta.
    Despite abundant current evidence that seems to reject the explanatory power of the market beta in expected returns, Shi and Xu assert that when tests are run with ICC as a proxy for expected return, the weaknesses seen by others are not necessarily present. They find that the future implied cost of capital is “both positively and significantly related to the conventional beta estimate,” implying that beta could still explain future cross-sectional expected return differences over a longer horizon. In other words, the conventional estimate of the market beta risk might be a good measure of the long-term market risk. As an example of how long-term expected return measures can be useful, Shi and Xu show a connection between individual stocks with high levels of uncertainty (i.e., an additional dimension of risk surrounding them as measured by analyst forecast dispersion) and long term expected returns as approximated by future ICC. Stocks with large dispersion—when analysts’ forecasts do not agree—tend to have high long-term expected return, suggesting that investors are compensated for taking on the uncertainty risk. Thus, not only is beta—or rather, long-term beta—of use when trying to forecast expected return, but it may bring with it unexpected positive results.
    CAPM may not be perfect, but it is intuitive, easy to apply, and powerful in practice. In fact, Shi and Xu’s research justifies the continued use by industry members of models such as Fama and French’s three-factor model, which includes the market factor as its most important factor. In addition, their results carry important implications across finance. For private equity firms and investment bankers assessing the value of a young company, what matters most? The answer (or at least what should be the answer) is the company’s growth potential and long-term expected return. The market risk beta value attached to the company is the risk associated with the company for many years to come. For portfolio managers struggling to rationalize, perhaps even to themselves, that a long-term conviction is worth holding, running numbers through a long-term prism can bring peace of mind and justify additional allocations. Shrewd investors will, of course, not just pay attention to a manager’s absolute performance, but also to their own level of beta risk. In addition, they will invest in strategies carrying levels of beta risk with which they can feel most comfortable.


    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.
  • Green Finance Forum II: Is ESG Integration a Fad, or Does It Have Alpha Potential?

    31 May 2017

    In Asia, the subject of ESG investing has been a very trendy topic.  As we all know, for many years, many investors have tried to incorporate elements of values and social responsibility into their investment strategies.  However, the return of these strategies has in the past left a lot to be desired.  It is natural to wonder why a rational investor would be willing to compromise the chances of superior performance in return for moral gratification.
    Well, past performance is not always a guide to future performance, and change is in the air.  More and more investors and asset owners are now placing increasing focus on ESG.  As an example, California State Teachers Retirement System, one of the largest asset owners in the world, has asked their fund managers to evaluate and assess 21 risk factors in each of their holdings, including, among others regulation, human rights, environmental and governance.

    On April 27, 2017, CFA Institute hosted a Green Finance Forum in Hong Kong to explore this issue. Full report on that event is attached.
  • Practitioner's Brief (video): ​The Power of Private Information

    25 May 2017

    Despite a recent crackdown on insider trading in China an assumption persists regarding the relative information inefficiency and asymmetry of less developed markets. Researcher Chi asks: How much is private information exploited in a less developed financial market like China?
    As it turns out, quite a lot.
  • Practitioner's Brief (Video):  Behind Closed Doors - How Private Meetings Move Public Markets

    25 May 2017

    The authors of a recent study on insider trading have taken a new, financial spin on the classic thought experiment that asks whether a falling tree makes any sound in an empty forest. Looking at how corporate insiders might use confidential information to make trades, they ask ‘If executives of publicly traded companies meet with investors, and no one from the public is around, does the information exchanged still influence the stock market?’ The results are striking.
  • Practitioner's Brief (Video):  Demystifying Seasonal Chinese Stock Return Synchronicity

    17 May 2017

    How much of a stock’s movement can be attributed to the movements in the index in which it resides? And if a stock moves in line with an index (or, in academic language, has a “high R squared”) what, exactly, accounts for that? Industry members and academics have numerous theories. The authors of a new paper tackle the question of synchronicity using earnings season in China, when the information on companies is more robust.
  • Practitioner’s Brief: Expect the Unexpected - Why Tightened Trading Rules Created a More Efficient Index Futures Market

    06 Apr 2017

    In the summer of 2015, Chinese regulators aggressively tightened fairly lax trading rules in the country’s stock index futures market in the midst of a chaotic crash. The move, an effort to tamp down rampant speculation and manipulation, was widely criticized as an overreach. With margin barriers thrown up higher and position sizes significantly capped for nonhedgers, speculators all but vanished (a desired outcome); however, so too did volume, which decreased nearly 100%—not exactly a best-case scenario. “China has killed the world’s biggest stock index futures market,” Bloomberg wrote in September 2015. Illiquidity in the market for futures tied to the China Security Index 300 “was causing problems,” the Financial Times said. In hindsight, it’s worth asking: Were these regulations, while apparently necessary, nevertheless ill-advised? No, assert the authors, who do not gloss over the fact that liquidity was severely impacted. What they do emphasize is a rather counterintuitive finding: A market in which liquidity has ground to a halt does have an upside.

    Eugene Fama’s efficient market hypothesis (EMH) holds that stock prices immediately and inherently reflect all available information such that there’s no predictive power to be gleaned, i.e., it’s all randomness at play. EMH has been endlessly tested and re-tested since it first emerged in the 1970s at a time when low-cost passive management was coming on the scene as a disruptor to active management. Several testing methods were used to batter/gut-check EMH. Prominent among them was the variance ratio (VR) test, put to use, alongside other tests, by authors Lin and Wang. They set out to measure the efficiency levels of the Chinese stock index futures between July 2015 and September 2015. During this period, a slew of rule changes were implemented, making it harder to trade index futures, a prevalent means of speculating, hedging, arbitraging, and as it turned out, carrying out manipulative schemes, e.g., pump-and-dumps or coordinated bear attacks. The futures market plays a major role in price discovery in the broader spot Chinese stock market. Prior to the change, rules for stock index futures trading were indeed loose and transaction costs were low. Leverage was plentiful and dangerously easy to access. When all of these conditions were curbed via tighter rules, something interesting happened. Yes, volume collapsed. But what happened to the market’s efficiency?

    The results of VR tests (and Granger causality tests) were puzzling. Although the authors thought they would find that regulatory tightening had a detrimental impact on market efficiency and price discovery, just as it had on volume and liquidity, it did not prove to be the case. To the contrary, the VR testing found that absolute VR levels of Chinese index futures’ five-minute returns went from roughly 2.70 before the rule change to around 1.0 after—a decrease of more than 50%. In other words, markets became more efficient in the post-tightening study period. With volume and liquidity in such a freefall, why would that happen? It is possible, explained the authors, that in low liquidity environments trading mainly occurs among the most knowledgeable institutional investors. Speculators and manipulators fall away. So we’re talking about very light trading—but among very well-informed participants free from the distractive din of the less informed. This hypothesis requires testing. If additional data was available, it would be an interesting topic for further research, according to the authors.

    WHAT ARE THE IMPLICATIONS FOR INVESTORS AND INVESTMENT PROFESSIONALS? “Regulators can be helpful in a bad market state,” the authors said, noting that at the time the rules were imposed the stock futures market had been overrun by unchecked manipulators who were abetted by low barriers to leverage and the ability to upsize. The regulatory goal of squeezing nonhedgers out of the market was met. Authorities drastically reduced excessive manipulation—without unintentionally creating a less efficient market. Co-author Hai Lin explained in an email: “While extreme regulations do not happen often, that doesn’t mean that their potential can be ignored.” Regulations can tighten in stressful environments—or in other words, right when investors might be most inclined to employ hedging strategies. In developing risk management strategies, investors need to view regulatory conditions as a factor that can vary over time. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.
  • Practitioner’s Brief: Paying Attention to Stock Ranking

    03 Apr 2017

    Investors have a limited amount of attention to give to their investment decisions. Many believe that paying attention to rankings of stocks provides them with a less attention demanding decision making shortcut. In reality, however, does paying attention to stocks ranked in a more salient place help improve financial decisions and market efficiency? Existing research on ranking and attention typically encounters the difficulty of separating out the pure effect of attention from that of fundamental news, which is especially true when rankings correlate with fundamentals. Thus, findings based on fundamental based rankings are subject to the confounding effects of both attention and fundamentals. The author tackles this challenge by exploring the price limit rule in China’s stock markets. Under that rule, stocks that hit the 10% upper price limit on a day are ranked by their daily returns, whose differentials are produced by mechanical rounding of maximum price changes as opposed to differential fundamental news. Investment practitioners in markets with the price limit rule in place may wish to exploit the impact of stock rankings for those stocks hitting the upper price limit. Thus, relevant questions to practitioners would pertain to (1) whether hitting the 10% upper price limit is truly an attention-grabbing event; (2) whether differential attention is allocated across the stocks hitting the price limit based on their rankings; and (3) whether stocks hitting the price limit that are ranked differently exhibit different subsequent returns, trading volume, volatility, and liquidity.

    The author conducted a series of tests to address the empirical questions posed above, studying a sample of China’s A-shares (shares that are quoted and traded in Chinese RMB) on China’s Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE). The time period is from 16 December 1996, when SSE and SZSE initially established the current price limit rule, to 31 March 2015, when the research project was initiated. The author selects 2,505 out of 4,910 trading days; during these days the markets had at least 5 upper price-limit events. We hereafter call stocks that hit the upper price limit on a day the “event” stocks. For most stocks, the daily absolute price movement is regulated to be 10% of the previous trading day’s closing price. When the 10% price change is not an integer number of cents, the daily price limit is rounded to the nearest cent. This creates different maximum returns allowed across stocks with different previous closing prices. For example, stocks with the previous closing price of RMB 9.99, RMB 10.00, and RMB 10.01 would all have a daily maximum price change of RMB 1.00; this results in a maximum return limit of 10.01%, 10.00%, and 9.99%, respectively. The differential maximum return limit is, therefore, caused by mechanical rounding and not by differential fundamental news. As a result, the author identifies the pure effect of ranking by exploring the differential attention paid to the more saliently ranked stock, which returns 10.01% in a day in the previous example, versus those with less salient rankings, which return 10% or 9.99%, and the implications of rankings for the financial market. To carry out the tests, the author hand collected the website viewer data from, one of the largest financial websites in China. The number of viewers from different IP addresses measures the attention paid by investors to a particular stock on a day. The author tested the impact of investor ranking by comparing two groups of event stocks, based on whether their event-day return was above median (usually 10%) or not, across dimensions of contemporaneous and subsequent returns, volume, volatility, and liquidity. Event stocks with the above median return were assigned to the high-rank group (with an average of above 12 stocks per day); the remainder were assigned to the low-rank group (with an average of near 10 stocks per day).

    The author finds that investors do pay significantly more attention to stocks hitting the price limit for several days on and after they hit the upper limit, measuring attention by the number of viewers on the webpage. The abnormally high attention persists for two weeks after the event day. Furthermore, the high-rank group receives even more attention than the low-rank group on the event day and the subsequent three days. Relative to the low-rank group, the high-rank group of event stocks experiences a greater price increase for two days as well as a greater price reversal that follows within one to two weeks. As for trading volume, liquidity, and return volatility, the results suggest that during the post-event period, the high-attention group of event stocks exhibits higher trading volume, better liquidity, and higher volatility. Smaller investors are more affected by the rank effect. Moreover, the effect of trading volume and volatility is larger and persists longer, but the effect of liquidity is smaller and lasts only for a few days. These effects are noticeably stronger when a larger number of stocks are hitting the upper price limit on a particular day—thus, investors are more attention constrained and top rankings are more salient. The author conducts similar analyses on stocks with a 5% price limit and stocks that hit the 10% lower price limit. The evidence is overall weak, suggesting that it is the rankings of stocks that hit the upper price limit that matter most for attention allocation. When the maximum return is not extreme enough to make the top ranking, or the ranking is for losers and mainly attracts sellers, neither strong investor attention nor the effect of attention is found.

    The findings send a clear message that even pure rankings that are uncorrelated with fundamentals dictate investor attention and lead to large and predictable effects on asset prices. Understanding how ranking affects asset prices will help to improve portfolio performance for institutional and individual investors who trade in securities markets where financial assets are presented in various ranking formats. Investors may consider exploring the temporary price momentum and subsequent price reversal of highly ranked stocks, and more importantly, exploring the return differential between high- and low-rank groups of stocks using a long-short portfolio. The conventional wisdom is that having investors who pay attention is a good thing; it means that important fundamental news is received and consumed, leading to more efficient asset prices. Advances in behavioural finance in recent years, however, suggest that attention may have a detrimental effect when it interplays with behavioural biases, such as the pure order effect. The findings of this article demonstrate such evidence as well as opportunities for smart investors who are paying attention in the right places. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Summarized by Danling Jiang and Jingyu Cui. Danling Jiang is an Associate Professor of Finance at Stony Brook University—SUNY and the Chang Jiang Scholar Visiting Professor at Southwest Jiaotong University. Jingyu Cui is a Master of Science in Finance student at Stony Brook University—SUNY.
  • Practitioner's Brief: Short-Sale Restrictions Imply Higher Returns

    02 Apr 2017

    Short-sale restriction is an important form of limits to arbitrage. In markets with partial shortsale restrictions, some stocks can be sold short (shortable stocks) whereas others cannot (no-short stocks). The latter are more subject to mispricing because of greater limits to arbitrage. Mispricing can be a source of risk, because investors may lose money if mispricing persists in the near term while they are betting against mispricing. Accordingly, finance theories predict that stocks more subject to mispricing—in this case, the no-short stocks—should on average earn a return premium as compensation for mispricing risk. Despite extensive research on short-sale constraints, few studies have directly tested the no-short return premium. Investment practitioners in markets with partial short-sale restrictions may want to exploit the no-short return premium induced by such regulations. To do so, they would ask two questions: Do the real-world data support the claim that no-short stocks on average earn higher returns? If so, how can they determine investment strategies based on the no-short return premium? The authors thus set out to reveal the superior expected excess and abnormal returns on no-short stocks over those on shortable stocks, as well as to demonstrate the strong return predictive power of the loadings on various long–short portfolios constructed using shortable and noshort stocks.

    The authors test their prediction about the no-short return premium using the Hong Kong stock market’s unique regulatory setting. In the Stock Exchange of Hong Kong (SEHK), stocks are periodically added to or deleted from the list of shortable stocks. This list is selected from the pool of stocks satisfying criteria based on market capitalization, liquidity, and so on. Stocks on the list are “shortable,” and stocks excluded from the list are “no-short”. The authors form a portfolio consisting only of no-short stocks (denoted as N) and a portfolio of only shortable stocks (denoted as S). They then create a long–short portfolio (denoted as NMS, for “no-short minus shortable”) as the return spread between N and S. They consider four different NMS portfolios, using the SEHK size and liquidity criteria to decide which stocks should be added to, can remain on, or should be removed from the official short-sale list. Further, the authors use Fama–MacBeth two-pass regressions to investigate how well the loadings of the test assets (portfolios and stocks) on each of the four NMS portfolios can predict the cross-section of future asset returns.

    The authors find that from 1997 through 2014, the NMS portfolio earns a monthly return of 2% to 3%, or an abnormal monthly return of about 1.3%, after accounting for its correlations with a set of standard common factors (market, size, value, liquidity, etc.). Thus, on average, no-short stocks indeed earn a return premium over shortable stocks. Moreover, the authors discover that no-short and shortable stocks tend to co-move negatively: When no-short stocks do better, shortable stocks tend to do worse. Mostly importantly, the factor loadings on the four NMS portfolios are strong positive predictors of future portfolio and stock returns in the cross-section. For example, the regression estimates imply that moving from the lowest 20% to the highest 20% NMS loading stocks increases the expected return next month by 1.5% to 2.0%. Moving from the lowest five shortable to the highest five no-short size and book-to-market portfolios increases the future average return by more than 4% per month. The loadings on the other three NMS portfolios, formed by considering the size and liquidity criteria for the official shorting list, exhibit similar or somewhat stronger forecast power.

    The findings will help to improve portfolio performance for institutional and individual investors who trade in securities markets with partial short-sale restrictions. Investors may consider gaining exposure to the NMS factor beyond their exposures to other standard factors. A refined strategy would require extracting stocks with the most extreme loadings on the NMS factor, as well as forming a portfolio that is long the highest NMS loading stocks and short the lowest NMS loading stocks. Furthermore, the refined trading strategy can be combined with strategies based on other style characteristics. Popular wisdom is that investors should pay more attention to the information revealed by short selling and take advantage of this information through observed short positions. The findings in this article direct investor attention to another side of the market, however: the stocks that cannot be sold short. These no-short stocks actually earn higher average returns because many investors may shy away from trading these more likely mispriced stocks. As a result, investors who are willing to invest in these stocks are paid to do so. For firms, however, regulation is bad news: Short-sale restrictions imply higher cost of equity. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    Summarized by Danling Jiang and Xiao-Ming Li. Danling Jiang is an associate professor of finance at SUNY at Stony Brook and the Chang Jiang Scholar Visiting Professor at Southwest Jiaotong University. Xiao-Ming Li is a professor of financial economics at the School of Economics and Finance (Albany), Massey University. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.
  • Practitioner’s Brief: Behind Closed Doors - How Private In-House Meetings Move Public Markets

    Tom Berry    Robert Bowen, Shantanu Dutta, Songlian Tang, Pengcheng (Phil) Zhu
    02 Apr 2017

    In this brief, we provide an investor’s-eye view of a piece of research that shines a floodlight on an inherently opaque subject—private meetings between senior management and investors. Both camps are presumed to know better than to share or receive anything that could be considered material non public information (MNPI). Nonetheless, the authors of this study point to some dubious trends associated with these sit-downs. The question of whether a falling tree makes noise in a forest devoid of hearing-enabled life forms has long held its own as a rudimentary philosophical riddle. But as a practical matter for debate, it’s not much of one, i.e., we’re pretty sure that in all likelihood, a tree crashing to the ground does make a sound. Now ponder this: If a private meeting between senior management and a fund manager takes place—and no one else is there to hear what’s said—are there consequences in the stock market? In other words, what is the point of these cozy sit-downs? Do the parties stand to benefit? Such meetings, of course, are routine and perfectly legal, provided the executives at the publicly traded company steer clear of disclosing any MNPI. The authors set out to ascertain, among other information, to what extent corporate insiders—who control the timing and content of meetings—trade on those meetings. “Overall, our results suggest that companies disclose material non-public information during these meetings and some participants trade on the information,” the authors state.

    The question of whether the meetings lead to some competitively advantageous information being leaked, maybe inadvertently, under the camouflage of crafty syntax, or even brazenly, might have remained one of mankind’s eternal mysteries had it not been for the Shenzhen Stock Exchange (SZSE). In 2009, the SZSE became the first exchange to require listed companies to report dates of private meetings with investors. Since August 2012, the SZSE has also required summary notes of what was said during those meetings, creating a dataset of some 17,000 meeting reports that the quartet of authors mined to startling effect. The authors found highly suspicious trading patterns among company insiders timing transactions ahead of and in the wake of private meetings. Although only 20% of private meetings can be connected with disclosed insider-trading activities, it is worth underscoring that the trades, some USD12 billion over a 28-month sample period (August 2012–December 2014), represent nearly two-thirds of the value of all insider trading among SZSE-listed companies during that time. Interestingly, nearly three-fourths of listed companies held at least one private meeting per year; the average was around five meetings per year. Most meetings were hosted in the companies’ headquarters.

    The research shows a clear trend of abnormally positive stock returns starting approximately 22 days prior to the private meeting dates. In fact, the average stock price run-up translates into RMB73.1 million (or about USD11 million) per average firm in the sample. Call it the “meeting anticipation effect” whereby investors/insiders trade on the not-irrational belief that in-house meetings generally reveal positive information. Some insiders appear to be selling into what they anticipate to be herd buying, using the increased volatility to mask their offloads.

    Many large institutional investors will undoubtedly scoff at the implication that they are gaming the system—or being gamed—by participating in face-to face conversations with the leaders of the companies in which they are investing large sums. These fund managers will also point to proprietary research processes that emphasize sophisticated models and, using the authors’ term, a “mosaic” of skillfully assembled information. Companies that hold meetings, likewise, could just as easily frame these interactions as transparent corporate citizenry, as evidenced by the high “information quality” scores enjoyed by the majority of the companies that report private meetings. The pieces are thus firmly in place for the facilitation of reinforced feedback loops: Companies that hold meetings have more analysts covering them, and these analysts represent large funds whose trades are closely watched. Insiders, who have seen this movie before, are not blind to the ripple effects of a few well-placed dollops of promising insinuations or even flat-out MNPI utterances. That there is an opportunity, thanks to the SZSE and the authors, for a sophisticated fund manager to write an algorithm scouring the mere record that meetings took place in an effort to catch some window of upside could be seen as one logical outcropping of the findings here, although we can think of another. Regulators in a developed market such as the United States might also find it useful to require some record of private meetings. Fund managers in the United States spend USD1.4 billion a year for face time with executives. The investment pays off well for those fund managers who are invited to these meetings and who make profitable trades around the meeting dates. According to the authors, the information gained from private in-house meetings provides these fund managers, and their investors, with an additional competitive edge. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.
  • Practitioner’s Brief: Demystifying Seasonal Chinese Stock Return Synchronicity

    Tom Berry    Jing Wang, Steven X. Wei, Wayne Yu
    02 Apr 2017

    How much of a stock’s movement can be attributed to movements in the index in which it resides? And, if a stock moves in line with an index (or, in academic parlance, has a "high R-squared") what, exactly, accounts for that co-movement? Industry members and academics have numerous theories: shared information being digested in lockstep by portfolio managers; index funds rebalancing themselves; or random noise—some inexplicable yet observable factor that for whatever reason creates an environment of stock-return synchronicity. For quant managers spotting trends, there’s no point in asking why; they just want the trend to repeat. Not so for researchers such as Wang, Wei, and Yu, who want to bring new perspective to this puzzle, one that has proved tricky for active fundamental managers seeking to differentiate themselves against the benchmark. In tackling the question of synchronicity, the authors explore the issue across a dynamic setting—earnings season in China, when more-robust information on companies becomes available—and also consider newer companies versus older ones.

    They examine synchronicity levels during earnings season, which for 98% of Chinese companies is January through April. The authors also go a step further, overlaying a variety of variables such as changes in fundamentals, fluctuating liquidity conditions, and corporate events. These events include any activity that could increase assets by 50% or more (e.g., a merger) and thus affect a company's systematic volatility.

    The authors discover that in rich information environments (i.e., earnings season), the degree of synchronicity (stocks moving in tandem) actually is reduced; and in less informative environments (non-earnings periods, May through December), synchronicity is more prevalent. This finding is noteworthy, if only in light of the overriding preconceptions about emerging markets such as China, long thought to generally be more prone to synchronous behavior relative to developed markets (for a host of reasons, including property rights considerations). Here, the authors are able to observe a repeated pattern: During Chinese earnings season, the degree of systematic volatility in that market is reduced. The trend is more pronounced for older companies with longer track records of meeting (or failing to meet) their numbers. One explanation stems from a concept that the authors call "intra-industry, cross-asset learning." Drilling down into this concept rather simplistically for illustration's sake, suppose three large companies from different industries (e.g., an automaker, a coal miner, and a retailer) make earnings announcements on the same day. Investors may make inferences about other companies in these respective industries. Now, further suppose that the following happens: The automaker’s earnings come in as expected; the coal miner’s come in better than expected; and the retailer’s do much worse than expected. In this example, one might expect share prices to behave distinctly among the three industries: mostly flat for auto firms, up for coal mining firms, and down for retailers. The market as a whole, however, may change little that day, with the offsetting share price changes in the different industries dampening the market or systematic volatility. In other words, share prices move in a less synchronized fashion because of intra-industry, cross-asset learning during the earnings announcement season, which reduces market or systematic volatility in the meantime.

    This study challenges the prevailing wisdom that Chinese stocks tend to move in step with each other, particularly with a time consideration (i.e., earnings season, when a higher intensity of firm-specific information arrives in the market). For stock pickers trying to differentiate themselves from a benchmark, earnings season would thus provide an especially opportune moment to show their ability to make a judgment call on a stock, take a position (bullish or bearish), and not have it mooted by the whims of the overall market. Conversely, for index investors, the authors’ findings suggest that the time to rebalance toward a passive approach would be during non-earnings season when Chinese stock market return synchronicity appears to be at a higher level. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the authors and do not represent the official views of CFA Institute or the authors’ employers.
  • Practitioner’s Brief: The Power of Private Information

    12 Mar 2017

    During the last 18 months, China has witnessed a flurry of securities fraud investigations and arrests involving a wide cross-section of the industry, from high-rolling financiers to humble accounting professors, and even some regulators. The insider-trading crackdown is partly an effort to wash away perceived stains of corruption following the crash of summer 2015. Even before that, though, Chinese insider trading cases had begun to mount as the market, and mechanisms to regulate it, matured. Despite its relative inexperience, China’s stock market is the second largest on earth. Still, an assumption persists (and is studied by researchers such as Chi) regarding the relative information inefficiency and asymmetry of less developed markets such as China. In his article, Chi makes no secret of his own perceptions about the pervasiveness of non-public information used for investing. One given in the hypothesis suggesting there are greater inefficiencies to potentially exploit in China relative to the United States is the fact that most Chinese mutual funds can outperform the index—certainly not the case in the United States. For Chi, then, the driving question becomes: To what extent is private information exploited in a less developed financial market such as China? To a significant degree, as it turns out. Chi’s research suggests that at a minimum, the insider buy is a powerful predictive tool for the generally upward direction of the stocks being bought, particularly for issues from state-owned enterprises (SOEs), and even more so for highly volatile stocks.

    Chi sets out to study insider trading in China via a proxy that, although an obvious choice, is nevertheless not to be conflated with criminal insider trading. That is, he looks at legal, disclosed trades made by corporate insiders, which, despite being purportedly aboveboard, still carry a connotation of information advantage. By creating a basic strategy to mimic insider buys, Chi demonstrates, at least on paper, the ability to add considerable alpha. Note that mimicking insider sells is not a good idea because sellers can have multiple motivations (e.g., liquidity or diversification needs). Buyers, on the other hand, generally are motivated by positive information. Mimicking insider buys may once have worked in the United States, when its stock market was nascent. Today, however, although by no means devoid of insider trading, the US market is viewed in academic terms as "efficient in semi-strong form." More informally, the system is not "rigged." If it were, Chi asserts, more US mutual fund managers would beat the index. In China, the perception of a rigged system became increasingly rampant after the summer of 2015 crash, as traders such as Xu Xiang (the Carl Ichan of China) seemed impervious to the market collapse when most other investors were crushed. Xu would later admit to conspiring with executives to control the timing of corporate announcements. To explore how private information is wielded in China, the author tapped the Wind Information database to study trading activity of corporate insiders (top executives, board members) between April 2007 and June 2014, focusing on the A-share market on two exchanges (Shanghai and Shenzhen) comprising some 2,555 stocks with a combined market cap (in 2013) of RMB20 trillion, or USD$3.3 trillion. The insiders' trading activity amounted to RMB900 billion, or 0.3% of total trading. Chi found the following: • Insiders reap large profits trading their company stocks. • Insider buys possess predictive power to stock prices; insider buys from SOEs have even stronger predicative power. • A rudimentary “mimicking-strategy” implemented for 12-month periods added 14.4% worth of annual alpha above the benchmark. And guess what else he found? The best-performing Chinese mutual funds' returns strongly correlated to the insider-mimicking strategy. Importantly, the fact that a fund trades in the same direction as insiders does not necessarily imply trading on material inside information. The author merely claims “that more correlated trading patterns point to a higher likelihood of private information shared by stock funds and corporate insiders." Because of data limitations, he cannot make a further claim about how fund managers obtain such private information.

    Before one delves into the art and science of insider-mimicking strategies, it is important to note an additional finding by the author. Chi split his six-year study into two three-year periods. In the latter period, the predicative power of the insider buy diminished significantly compared with the first period. So, as time passed, the Chinese market appears to have become more, not less, efficient. Here’s one last bit of material information that is hardly any secret: China’s recent insider trading crackdown will serve only to accelerate this trend. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    Summarized by Rich Blake. Rich is a veteran financial journalist who has written for numerous media outlets, including Reuters, ABC News and Institutional Investor. The views expressed herein reflect those of the author(s) and do not represent the official views of CFA Institute or the authors’ employers.
  • Changes in Financial Regulation in the Time of Trump: Financial Choice Act

    22 Dec 2016

    This is a blog article from CFA Institute Market Insight Blog, posted on 18 November 2016.
  • CorpGov Roundup: Transparency a Common Denominator in Recent Governance Reforms

    21 Dec 2016

    This is a blog article from CFA Institute Market Integrity Insights, posted on 8 December 2016.
  • Dimensional Fund Advisors

    05 Dec 2016

    This is one of the presentation powerpoint in CFA China Conference on 20 August 2016.

    05 Dec 2016

    This is one of the presentation powerpoint in CFA China Conference on 20 August 2016.
  • The Fixed-Income Challenge: The Monetary Policy Pit

    14 Oct 2016

    This is a blog posted on CFA Institute's website on 15 September 2016.
  • Daniel Goleman: Three Steps to Better Investing

    14 Oct 2016

    It is a blog posted on CFA Institute's website on 20 September 2016
  • Diversity in Finance: It Matters

    14 Oct 2016

    This is a blog posted on CFA Institute's website on 26 September 2016.
  • 100 Small Steps: Will India’s Bank Licenses Bring Reform?

    14 Oct 2016

    This is a blog posted on CFA Institute's website on 30 September 2016.
  • Are Commonsense Principles of Corporate Governance Any Good? Yes, They Are

    15 Aug 2016

    This is a blog posted on CFA Institute's website on 12 August 2016.
  • CorpGov Roundup: Has Brexit Ushered in Monumental Change to UK Corporate Governance?

    15 Aug 2016

    This is a blog posted on CFA Institute's website on 9 August 2016.
  • CorpGov Roundup: Is US Proxy Advisory Industry under Attack?

    15 Aug 2016

    This is a blog posted on CFA Institute's website on 6 July 2016.
  • Code and Standards: Are You Living Up To Your Annual Pledge?

    15 Aug 2016

    This is a blog posted on CFA Institute's website on 29 June 2016.